Technical Knowledge Manager

BioCatch is the leader in Behavioral Biometrics, a technology that leverages machine learning to analyze an online user’s physical and cognitive digital behavior to protect individuals online. BioCatch’s mission is to unlock the power of behavior and deliver actionable insights to create a digital world where identity, trust, and ease coexist. Today, 32 of the world's largest 100 banks and 210 total financial institutions rely on BioCatch Connect™ to combat fraud, facilitate digital transformation, and grow customer relationships. BioCatch’s Client Innovation Board, an industry-led initiative including American Express, Barclays, Citi Ventures, and National Australia Bank, helps BioCatch to identify creative and cutting-edge ways to leverage the unique attributes of behavior for fraud prevention. With over a decade of analyzing data, more than 80 registered patents, and unparalleled experience, BioCatch continues to innovate to solve tomorrow’s problems. For more information, please visit www.biocatch.com

We are looking for a highly skilled Technical Knowledge Manager to join our unique, multidisciplinary Knowledge & Enablement team. This team oversees the company’s knowledge operations and learning platforms, both internally and customer-facing.

We're not hiring a "technical writer". We're hiring someone who thinks of knowledge as a product — one with architecture, users, feedback loops, and a lifecycle. You'll own how our technology is understood: by customers integrating our B2B platform, by engineers documenting their own work, and by internal teams making decisions in a fast-moving R&D environment.

What You’ll Be Doing

• Design and maintain the external documentation system: API references, integration guides, onboarding flows, and troubleshooting content — treating it as a living product with real users and measurable outcomes.
• Define AI-augmented documentation workflows: where LLMs assist with drafting, where they validate accuracy, and where human judgment is irreplaceable.
• Work directly with engineers, PMs, and researchers to extract knowledge: you interview, synthesize, and structure what others know but haven't written down.
• Own content quality signals: broken links, outdated docs, low-rated articles, search dead-ends. You set up the loops that catch decay before customers do.
• Contribute to the information architecture: taxonomy, versioning strategy, content type standards, and the toolchain that supports them.
• Lead content localization efforts, including multilingual adaptations, knowledge base structuring, and format alignment.
• Manage the creation and distribution of release notes in collaboration with Product and R&D teams.
• Contribute to building and stewarding the internal knowledge base for our own employees: capturing decision logs, architecture notes, research summaries, and runbooks so institutional knowledge doesn't live only in people's heads.

• Comfortable reading code, API specs, and architecture diagrams: not necessarily writing production code, but enough to work as a credible peer to engineers.
• Understands developer experience (DX) and user experience (UX) as a discipline: knows what makes technical, operational, and similar docs actually usable.
• Uses LLMs as a force multiplier: for first drafts, translation, summarization, and doc-from-code generation, while knowing when output needs expert review.
• Has hands-on experience with at least one AI-assisted docs workflow (e.g., GPT, Confluence, Claude, Notion, custom RAG on internal wikis or similar).
• Think critically about AI-generated content: hallucination risk, freshness, attribution, and user trust.
• Passion for knowledge management and sharing.
• Experience in technical writing, knowledge management, or a similar role.
• Experience in multi-disciplinary content creation and management within a B2B SaaS company.
• Strong attention to detail with the ability to manage multiple projects simultaneously.
• Excellent interpersonal and communication skills, with the ability to collaborate across teams.
• Native-level English proficiency is required; proficiency in additional languages is an advantage.
• A bachelor’s degree in a related field is an advantage.
• Tools & ecosystem (nice to have, not a checklist): Docs platforms: Confluence, Document360, GitBook, Notion. AI tooling: Claude, Copilot, OpenAI, Cursor, custom LLM pipelines, prompt engineering for structured output. Spec formats: OpenAPI / Swagger, AsyncAPI, GraphQL schema docs